Jing Li is an associate professor of industrial engineering in the School of Computing, Informatics, and Decision Systems Engineering in the Ira A. Fulton Schools of Engineering at Arizona State University. She is also co-founder for the ASU-Mayo Center for Innovative Imaging. Her research develops statistical models and machine learning algorithms to tackle the data science challenges rising from health and engineering domains. Her specific areas of expertise in statistics and machine learning include transfer learning, multitask learning, sparse models, graphical models, and data and model fusion. She has been conducting research at the interface between data science and health and medicine to tackle imaging, genomics and other clinical datasets to provide solutions to diagnosis, subtyping, prognosis, telemonitoring and radiogenomics for a number of neurological diseases (migraine, traumatic brain injury, Alzheimer's disease, Parkinson's disease) and cancer (glioblastoma, breast, prostate, lung). Her research is sponsored by NIH, NSF, DOD, Arizona State, and Mayo Clinic. She is a National Science Founcation (NSF) CAREER Awardee.

Li is a former chair for the Data Mining Subdivision of INFORMS. She is currently the editor-in-chief for Quality Technology and Quantitative Management, an associate editor for IISE Transactions on Healthcare Systems Engineering, and on the editorial board of Journal of Quality Technology.